Quality-Driven Autoencoder for Nonlinear Quality-Related and Process-Related Fault Detection Based on Least-Squares Regularization and Enhanced Statistics

Yan, Shifu; Yan, Xuefeng

doi:10.1021/acs.iecr.0c00735

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…After all the process variables are assigned, the input matrix X is divided into two parts: the quality-related part, X sel , and quality-unrelated part, X res X = X sel + X res (11) According to the preliminary part of PLS above, the regression coefficient between the latent variable and the quality variable is…”

Section: The Proposed Mi-plsmentioning

confidence: 99%

“…According to the prior knowledge of these faults, there were ten quality-related faults (IDV 1, 2, 5-8, 12, 13, 18, and 21) and eleven faults unrelated to quality (IDV 3,4,(9)(10)(11)14,15,16,17,19 and 20). The prediction result is a suitable way to illustrate the model's accuracy.…”

Section: Tennessee Eastman Process Simulationmentioning

confidence: 99%

“…Offline optimization aims to minimize prediction error and variance or optimize the probability density function of the prediction error, and uses gradient descent, conjugate gradient, and intelligent optimization methods, among others [9], to obtain the optimal solution. Online optimization methods are based on a comparison between the predicted value at each step and the real value, and simulation models in the Simulink environment [10][11][12]. The extraction method is used to predict the error compensation to make online corrections to the next predicted value and continuously updates the model parameters to achieve a rolling optimization.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

2021

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Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

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Section: The Proposed Mi-plsmentioning

confidence: 99%

Section: Tennessee Eastman Process Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

2021

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“…The variables in this process contain two blocks of variables: the XMV block of 11 manipulated variables and the XMEAS block of 41 measured variables which include 22 process and 19 analysis variables. In this simulation, 22 process variables (XMEAS ) and 11 manipulated variables (XMV (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)) are chosen to be process input X, and select purge gas analysis component G (XMEAS (35)) as the quality output Y.…”

Section: Te Benchmarkmentioning

confidence: 99%

“…Other process-related fault detection methods have canonical correlation analysis (CCA), 6 non-negative matrix factorization (NMF), 7,8 and so on. Another particularly important research direction for MSPM is quality-related fault diagnosis, [9][10][11] in which qualityrelated fault detection and fault isolation are two key tasks. Quality-related fault detection belongs to a supervised learning task in machine learning area, 12 and it aims to detect whether a fault that affects product quality occurs in the industrial system.…”

Section: Introductionmentioning

confidence: 99%

Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Ren

Tang

Zhang

2021

International Journal of Distributed Sensor Networks

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The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.

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Output-related fault detection in non-stationary processes using constructive correlative-SAE and demoting correlative-DNN

Rashidi

Zhao

2022

Applied Soft Computing

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Quality-Driven Autoencoder for Nonlinear Quality-Related and Process-Related Fault Detection Based on Least-Squares Regularization and Enhanced Statistics

Cited by 12 publications

References 35 publications

A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

Quality-related fault diagnosis based onk-nearest neighbor rule for non-linear industrial processes

Output-related fault detection in non-stationary processes using constructive correlative-SAE and demoting correlative-DNN

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